LEADER 00890nam0-22003131i-450- 001 990002023000403321 005 20021010 035 $a000202300 035 $aFED01000202300 035 $a(Aleph)000202300FED01 035 $a000202300 100 $a20021010d--------km-y0itay50------ba 101 0 $aita 200 1 $a<>Biologie des Blattlaus-Generationswechsels$emit besonderer Berücksichtigung terminologischer Aspekte$fGerolf Lamper 210 $aJena$cGustav Fischer$d1968 215 $a264 p.$d24 cm 610 0 $aOmotteri 610 0 $aRincoti 610 0 $aAfidi 676 $a595.752 700 1$aLampel,$bGerolf$085826 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002023000403321 952 $a61 IV F.5/41$b3030$fDAGEN 959 $aDAGEN 996 $aBiologie des Blattlaus-Generationswechsels$9405292 997 $aUNINA DB $aING01 LEADER 01475nam 2200325z- 450 001 9910346922803321 005 20210212 035 $a(CKB)4920000000101294 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/58557 035 $a(oapen)doab58557 035 $a(EXLCZ)994920000000101294 100 $a20202102d2009 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRobust automatic transcription of lectures 210 $cKIT Scientific Publishing$d2009 215 $a1 online resource (XXVI, 173 p. p.) 311 08$a3-86644-394-3 330 $aAutomatic transcription of lectures is becoming an important task. Possible applications can be found in the fields of automatic translation or summarization, information retrieval, digital libraries, education and communication research. Ideally those systems would operate on distant recordings, freeing the presenter from wearing body-mounted microphones. This task, however, is surpassingly difficult, given that the speech signal is severely degraded by background noise and reverberation. 610 $aautomatic speech recognition 610 $afeature enhancement 610 $aspeech signal processing 700 $aWölfel$b Matthias$4auth$01117265 906 $aBOOK 912 $a9910346922803321 996 $aRobust automatic transcription of lectures$93025752 997 $aUNINA